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Zhai T. Druggable genome-wide Mendelian randomization for identifying the role of integrated stress response in therapeutic targets of bipolar disorder. J Affect Disord 2024; 362:843-852. [PMID: 39025441 DOI: 10.1016/j.jad.2024.07.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 06/13/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
For bipolar disorder (BD), the inconsistency of treatment guidelines and the long phases of pharmacological adjustment remain major challenges. BD is known to be comorbid with many medical and psychiatric conditions and they may share inflammatory and stress-related aetiologies, which could give rise to this association. The integrated stress response (ISR) responds to various stress conditions that lead to alterations in cellular homeostasis. However, as a causative mechanism underlying cognitive deficits and neurodegeneration in a broad range of brain disorders, the impact of ISR on BD is understudied. Mendelian randomization has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for BD and analyze their pathophysiological mechanisms, using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41,917 BD cases and 371,549 controls from the Psychiatric Genomics Consortium and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. The SMR analysis identified the EIF2B5 gene that was associated with BD due to no linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. Utilizing data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritize potential alternative drug targets.
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Affiliation(s)
- Ting Zhai
- School of Humanities, Southeast University, Nanjing 211189, China; Institute of Child Development and Education, Southeast University, Nanjing 211189, China; Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing 211189, China.
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2
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Lee SHT, Garske KM, Arasu UT, Kar A, Miao Z, Alvarez M, Koka A, Darci-Maher N, Benhammou JN, Pan DZ, Örd T, Kaminska D, Männistö V, Heinonen S, Wabitsch M, Laakso M, Agopian VG, Pisegna JR, Pietiläinen KH, Pihlajamäki J, Kaikkonen MU, Pajukanta P. Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease. EBioMedicine 2024; 106:105232. [PMID: 38991381 DOI: 10.1016/j.ebiom.2024.105232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD). METHODS To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants. FINDINGS We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue. INTERPRETATION Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis. FUNDING NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.
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Affiliation(s)
- Seung Hyuk T Lee
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kristina M Garske
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Asha Kar
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Zong Miao
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Marcus Alvarez
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Amogha Koka
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nicholas Darci-Maher
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jihane N Benhammou
- Vatche and Tamar Manoukian Division of Digestive Diseases and Gastroenterology, Hepatology and Parenteral Nutrition, David Geffen School of Medicine at UCLA and VA Greater Los Angeles HCS, Los Angeles, CA, USA
| | - David Z Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Tiit Örd
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorota Kaminska
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Division of Cardiology, Department of Medicine, UCLA, Los Angeles, CA, USA
| | - Ville Männistö
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland; Department of Internal Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Sini Heinonen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Martin Wabitsch
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics and Adolescent Medicine, University of Ulm, Ulm, Germany
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Vatche G Agopian
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Joseph R Pisegna
- Department of Medicine and Human Genetics, Division of Gastroenterology, Hepatology and Parenteral Nutrition, David Geffen School of Medicine at UCLA and VA Greater Los Angeles HCS, Los Angeles, CA, USA
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Healthy WeightHub, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland; Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA; Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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3
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JL, Civelek M. Systems genetics analysis of human body fat distribution genes identifies adipocyte processes. Life Sci Alliance 2024; 7:e202402603. [PMID: 38702075 PMCID: PMC11068934 DOI: 10.26508/lsa.202402603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.
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Affiliation(s)
- Jordan N Reed
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jiansheng Huang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Yong Li
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Lijiang Ma
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dhanush Banka
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Martin Wabitsch
- Division of Paediatric Endocrinology and Diabetes, Department of Paediatrics and Adolescent Medicine, Ulm University Medical Centre, Ulm, Germany
| | - Tianfang Wang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Wen Ding
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Johan Lm Björkegren
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Mete Civelek
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
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4
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Martin L, Boutwell BB, Messerlian C, Adams CD. Mendelian randomization reveals apolipoprotein B shortens healthspan and possibly increases risk for Alzheimer's disease. Commun Biol 2024; 7:230. [PMID: 38402277 PMCID: PMC10894226 DOI: 10.1038/s42003-024-05887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/05/2024] [Indexed: 02/26/2024] Open
Abstract
Apolipoprotein B-100 (APOB) is a component of fat- and cholesterol-transporting molecules in the bloodstream. It is the main lipoprotein in low-density lipoprotein cholesterol (LDL) and has been implicated in conditions that end healthspan (the interval between birth and onset of chronic disease). However, APOB's direct relationship with healthspan remains uncertain. With Mendelian randomization, we show that higher levels of APOB and LDL shorten healthspan in humans. Multivariable Mendelian randomization of APOB and LDL on healthspan suggests that the predominant trait accounting for the relationship is APOB. In addition, we provide preliminary evidence that APOB increases risk for Alzheimer's disease, a condition that ends healthspan. If these relationships are causal, they suggest that interventions to improve healthspan in aging populations could include strategies targeting APOB. Ultimately, given that more than 44 million people currently suffer from Alzheimer's disease worldwide, such interventions are needed.
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Affiliation(s)
- Leah Martin
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Brian B Boutwell
- School of Applied Sciences, University of Mississippi, University, Jackson, MS, USA
- John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Carmen Messerlian
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Obstetrics and Gynecology, Massachusetts General Hospital Fertility Center, Boston, MA, USA
- Department of Environmental Health, Harvard T. H. Chan School of Public Heath, Boston, MA, USA
| | - Charleen D Adams
- Department of Environmental Health, Harvard T. H. Chan School of Public Heath, Boston, MA, USA.
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5
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Mapel XM, Kadri NK, Leonard AS, He Q, Lloret-Villas A, Bhati M, Hiltpold M, Pausch H. Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle. Nat Commun 2024; 15:674. [PMID: 38253538 PMCID: PMC10803364 DOI: 10.1038/s41467-024-44935-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.
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Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Qiongyu He
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | | | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Maya Hiltpold
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland.
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6
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Brotman SM, El-Sayed Moustafa JS, Guan L, Broadaway KA, Wang D, Jackson AU, Welch R, Currin KW, Tomlinson M, Vadlamudi S, Stringham HM, Roberts AL, Lakka TA, Oravilahti A, Silva LF, Narisu N, Erdos MR, Yan T, Bonnycastle LL, Raulerson CK, Raza Y, Yan X, Parker SCJ, Kuusisto J, Pajukanta P, Tuomilehto J, Collins FS, Boehnke M, Love MI, Koistinen HA, Laakso M, Mohlke KL, Small KS, Scott LJ. Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.563798. [PMID: 37961277 PMCID: PMC10634839 DOI: 10.1101/2023.10.26.563798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with ≥2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.
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Affiliation(s)
- Sarah M Brotman
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | | | - Li Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Dongmeng Wang
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kevin W Currin
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Max Tomlinson
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | | | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael R Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tingfen Yan
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lori L Bonnycastle
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Yasrab Raza
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Xinyu Yan
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Stephen C J Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Johanna Kuusisto
- Department of Medicine and Clinical Research, Kuopio University Hospital, Kuopio, Finland
| | - Päivi Pajukanta
- Department of Human Genetics and Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jaakko Tuomilehto
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Heikki A Koistinen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki and Department of Medicine, Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Medicine and Clinical Research, Kuopio University Hospital, Kuopio, Finland
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
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Abstract
Obesity is a common complex trait that elevates the risk for various diseases, including type 2 diabetes and cardiovascular disease. A combination of environmental and genetic factors influences the pathogenesis of obesity. Advances in genomic technologies have driven the identification of multiple genetic loci associated with this disease, ranging from studying severe onset cases to investigating common multifactorial polygenic forms. Additionally, findings from epigenetic analyses of modifications to the genome that do not involve changes to the underlying DNA sequence have emerged as key signatures in the development of obesity. Such modifications can mediate the effects of environmental factors, including diet and lifestyle, on gene expression and clinical presentation. This review outlines what is known about the genetic and epigenetic contributors to obesity susceptibility, along with the albeit limited therapeutic options currently available. Furthermore, we delineate the potential mechanisms of actions through which epigenetic changes can mediate environmental influences and the related opportunities they present for future interventions in the management of obesity.
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Affiliation(s)
- Khanh Trang
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Diabetes and Endocrinology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104 USA
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8
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Anwar MY, Graff M, Highland HM, Smit R, Wang Z, Buchanan VL, Young KL, Kenny EE, Fernandez-Rhodes L, Liu S, Assimes T, Garcia DO, Daeeun K, Gignoux CR, Justice AE, Haiman CA, Buyske S, Peters U, Loos RJF, Kooperberg C, North KE. Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts. Hum Genet 2023; 142:1477-1489. [PMID: 37658231 DOI: 10.1007/s00439-023-02593-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/10/2023] [Indexed: 09/03/2023]
Abstract
Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.
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Affiliation(s)
- Mohammad Yaser Anwar
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Roelof Smit
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Victoria L Buchanan
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA, 16802, USA
| | - Simin Liu
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI, 02903, USA
| | - Themistocles Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - David O Garcia
- Department of Health Promotion Sciences, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85724, USA
| | - Kim Daeeun
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health, Danville, PA, 17822, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JLM, Civelek M. Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556534. [PMID: 37732278 PMCID: PMC10508754 DOI: 10.1101/2023.09.06.556534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach. METHODS We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA. RESULTS 51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression. SUMMARY ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.
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10
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Rouskas K, Katsareli EA, Amerikanou C, Dimopoulos AC, Glentis S, Kalantzi A, Skoulakis A, Panousis N, Ongen H, Bielser D, Planchon A, Romano L, Harokopos V, Reczko M, Moulos P, Griniatsos I, Diamantis T, Dermitzakis ET, Ragoussis J, Dedoussis G, Dimas AS. Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population. BMC Genomics 2023; 24:442. [PMID: 37543566 PMCID: PMC10403965 DOI: 10.1186/s12864-023-09532-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/25/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. RESULTS We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. CONCLUSIONS By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.
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Affiliation(s)
- Konstantinos Rouskas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thessaloniki, Greece
| | - Efthymia A Katsareli
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Charalampia Amerikanou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Alexandros C Dimopoulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Hellenic Naval Academy, Hatzikyriakou Avenue, Pireaus, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children's Hospital, Athens, Greece
| | - Alexandra Kalantzi
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Anargyros Skoulakis
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | | | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
- Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
- Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Deborah Bielser
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Alexandra Planchon
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Vaggelis Harokopos
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Martin Reczko
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
| | - Panagiotis Moulos
- Institute for Fundamental Biomedical Science, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece
- Center of New Biotechnologies & Precision Medicine, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Griniatsos
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Theodoros Diamantis
- First Department of Surgery, National and Kapodistrian University of Athens, Medical School, Laiko Hospital, Athens, Greece
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University Genome Centre, McGill University, Montréal, QC, Canada
- Department of Bioengineering, McGill University, Montréal, QC, Canada
| | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center 'Alexander Fleming', Vari, Greece.
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11
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Fabo T, Khavari P. Functional characterization of human genomic variation linked to polygenic diseases. Trends Genet 2023; 39:462-490. [PMID: 36997428 PMCID: PMC11025698 DOI: 10.1016/j.tig.2023.02.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/30/2023]
Abstract
The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.
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Affiliation(s)
- Tania Fabo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Paul Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA; Stanford Cancer Institute, Stanford University, Stanford, CA, USA; Graduate Program in Genetics, Stanford University, Stanford, CA, USA; Stanford University School of Medicine, Stanford University, Stanford, CA, USA; Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA.
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12
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Jain N, Richter F, Adzhubei I, Sharp AJ, Gelb BD. Small open reading frames: a comparative genetics approach to validation. BMC Genomics 2023; 24:226. [PMID: 37127568 PMCID: PMC10152738 DOI: 10.1186/s12864-023-09311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/13/2023] [Indexed: 05/03/2023] Open
Abstract
Open reading frames (ORFs) with fewer than 100 codons are generally not annotated in genomes, although bona fide genes of that size are known. Newer biochemical studies have suggested that thousands of small protein-coding ORFs (smORFs) may exist in the human genome, but the true number and the biological significance of the micropeptides they encode remain uncertain. Here, we used a comparative genomics approach to identify high-confidence smORFs that are likely protein-coding. We identified 3,326 high-confidence smORFs using constraint within human populations and evolutionary conservation as additional lines of evidence. Next, we validated that, as a group, our high-confidence smORFs are conserved at the amino-acid level rather than merely residing in highly conserved non-coding regions. Finally, we found that high-confidence smORFs are enriched among disease-associated variants from GWAS. Overall, our results highlight that smORF-encoded peptides likely have important functional roles in human disease.
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Affiliation(s)
- Niyati Jain
- Department of Genetics and Genomic Sciences and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount, Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY, 10029, USA
- Present Address: Committee On Genetics, Genomics, and Systems Biology, The University of Chicago, Chicago, IL, USA
| | - Felix Richter
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ivan Adzhubei
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew J Sharp
- Department of Genetics and Genomic Sciences and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount, Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Bruce D Gelb
- Department of Genetics and Genomic Sciences and Mindich Child Health and Development Institute, Icahn School of Medicine at Mount, Hess Center for Science and Medicine, 1470 Madison Avenue, New York, NY, 10029, USA.
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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13
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Aherrahrou R, Lue D, Perry RN, Aberra YT, Khan MD, Soh JY, Örd T, Singha P, Yang Q, Gilani H, Benavente ED, Wong D, Hinkle J, Ma L, Sheynkman GM, den Ruijter HM, Miller CL, Björkegren JLM, Kaikkonen MU, Civelek M. Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes. Circ Res 2023; 132:323-338. [PMID: 36597873 PMCID: PMC9898186 DOI: 10.1161/circresaha.122.321586] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 12/20/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies have identified over 175 loci associated with CAD. The majority of these loci are in noncoding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we aimed to identify the subset of the CAD loci associated with the regulation of transcription in distinct SMC phenotypes. METHODS We measured gene expression in SMCs isolated from the ascending aortas of 151 heart transplant donors of various genetic ancestries in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single-nucleotide polymorphism markers across the genome. RESULTS We identified 4910 expression and 4412 splicing quantitative trait loci (sQTLs) representing regions of the genome associated with transcript abundance and splicing. A total of 3660 expression quantitative trait loci (eQTLs) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 eQTLs were SMC-specific and sex-biased, respectively. We made these results available for public query on a user-friendly website. To identify the effector transcript(s) regulated by CAD loci, we used 4 distinct colocalization approaches. We identified 84 eQTL and 164 sQTL that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. One CAD locus colocalized with a sex-specific eQTL (TERF2IP), and another locus colocalized with SMC-specific eQTL (ALKBH8). The most significantly associated CAD locus, 9p21, was an sQTL for the long noncoding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. CONCLUSIONS Collectively, our results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.
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Affiliation(s)
- Rédouane Aherrahrou
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Dillon Lue
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - R Noah Perry
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Yonathan Tamrat Aberra
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Mohammad Daud Khan
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Joon Yuhl Soh
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Tiit Örd
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Prosanta Singha
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Qianyi Yang
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Huda Gilani
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ernest Diez Benavente
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jameson Hinkle
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, United States of America
| | - Gloria M Sheynkman
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Cancer Center, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, The Netherlands
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Johan LM Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States of America
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, United States of America
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | - Minna U Kaikkonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mete Civelek
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
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14
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Brown M, Greenwood E, Zeng B, Powell JE, Gibson G. Effect of all-but-one conditional analysis for eQTL isolation in peripheral blood. Genetics 2023; 223:iyac162. [PMID: 36321965 PMCID: PMC9836021 DOI: 10.1093/genetics/iyac162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
Expression quantitative trait locus detection has become increasingly important for understanding how noncoding variants contribute to disease susceptibility and complex traits. The major challenges in expression quantitative trait locus fine-mapping and causal variant discovery relate to the impact of linkage disequilibrium on signals due to one or multiple functional variants that lie within a credible set. We perform expression quantitative trait locus fine-mapping using the all-but-one approach, conditioning each signal on all others detected in an interval, on the Consortium for the Architecture of Gene Expression cohorts of microarray-based peripheral blood gene expression in 2,138 European-ancestry human adults. We contrast these results with traditional forward stepwise conditional analysis and a Bayesian localization method. All-but-one conditioning significantly modifies effect-size estimates for 51% of 2,351 expression quantitative trait locus peaks, but only modestly affects credible set size and location. On the other hand, both conditioning approaches result in unexpectedly low overlap with Bayesian credible sets, with just 57% peak concordance and between 50% and 70% SNP sharing, leading us to caution against the assumption that any one localization method is superior to another. We also cross reference our results with ATAC-seq data, cell-type-specific expression quantitative trait locus, and activity-by-contact-enhancers, leading to the proposal of a 5-tier approach to further reduce credible set sizes and prioritize likely causal variants for all known inflammatory bowel disease risk loci active in immune cells.
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Affiliation(s)
- Margaret Brown
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Emily Greenwood
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Biao Zeng
- Present address for Biao Zeng: Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joseph E Powell
- Present address for Joseph E Powell: Garvan-Weizmann Center for Cellular Genomics, Sydney, NSW 2010, Australia
| | - Greg Gibson
- Center for Integrative Genomics, School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
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15
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Wolter JM, Le BD, Matoba N, Lafferty MJ, Aygün N, Liang D, Courtney K, Song J, Piven J, Zylka MJ, Stein JL. Cellular Genome-wide Association Study Identifies Common Genetic Variation Influencing Lithium-Induced Neural Progenitor Proliferation. Biol Psychiatry 2023; 93:8-17. [PMID: 36307327 PMCID: PMC9982734 DOI: 10.1016/j.biopsych.2022.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/22/2022] [Accepted: 08/18/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND Bipolar disorder is a highly heritable neuropsychiatric condition affecting more than 1% of the human population. Lithium salts are commonly prescribed as a mood stabilizer for individuals with bipolar disorder. Lithium is clinically effective in approximately half of treated individuals, and their genetic backgrounds are known to influence treatment outcomes. While the mechanism of lithium's therapeutic action is unclear, it stimulates adult neural progenitor cell proliferation, similar to some antidepressant drugs. METHODS To identify common genetic variants that modulate lithium-induced proliferation, we conducted an EdU incorporation assay in a library of 80 genotyped human neural progenitor cells treated with lithium. These data were used to perform a genome-wide association study to identify common genetic variants that influence lithium-induced neural progenitor cell proliferation. We manipulated the expression of a putatively causal gene using CRISPRi/a (clustered regularly interspaced short palindromic repeats interference/activation) constructs to experimentally verify lithium-induced proliferation effects. RESULTS We identified a locus on chr3p21.1 associated with lithium-induced proliferation. This locus is also associated with bipolar disorder risk, schizophrenia risk, and interindividual differences in intelligence. We identified a single gene, GNL3, whose expression temporally increased in an allele-specific fashion following lithium treatment. Experimentally increasing the expression of GNL3 led to increased proliferation under baseline conditions, while experimentally decreasing GNL3 expression suppressed lithium-induced proliferation. CONCLUSIONS Our experiments reveal that common genetic variation modulates lithium-induced neural progenitor proliferation and that GNL3 expression is necessary for the full proliferation-stimulating effects of lithium. These results suggest that performing genome-wide associations in genetically diverse human cell lines is a useful approach to discover context-specific pharmacogenomic effects.
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Affiliation(s)
- Justin M Wolter
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Brandon D Le
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nana Matoba
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Michael J Lafferty
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nil Aygün
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Dan Liang
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kenan Courtney
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Juan Song
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Mark J Zylka
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jason L Stein
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
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16
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Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle. Int J Mol Sci 2022; 23:ijms232315055. [PMID: 36499383 PMCID: PMC9736101 DOI: 10.3390/ijms232315055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world’s food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs’ proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.
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17
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He D, Meng P, Li C, Jia Y, Wen Y, Pan C, Zhang Z, Zhang J, Zhang H, Chen Y, Zhao Y, Qin X, Cai Q, Wei W, Shi S, Chu X, Zhang N, Zhang F. Association between telomere length and insomnia: A mendelian randomization and colocalization study. Sleep Med 2022; 100:304-310. [PMID: 36182724 DOI: 10.1016/j.sleep.2022.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
BACKGROUND Previous studies have suggested a potential association between sleep and telomere length (TL), but its genetic basis remains unclear. In this study, we aimed to explore the genetic correlation and potential causal association between TL and insomnia. METHODS The genome-wide association study (GWAS) datasets of TL and insomnia-related traits were used, including insomnia, snoring, daytime dozing and napping. Based on the polygenic risk scores (PRS) of TL, linear regression and linkage disequilibrium score (LDSC) regression were used to preliminarily explore the association between TL and insomnia parameters in the UK Biobank cohort. Then, we investigated the causal association between TL and insomnia by mendelian randomization (MR) analysis and colocalization analysis. RESULTS In the UK Biobank cohort, the association between TL and insomnia was observed in the female samples (t = 2.968, P = 3.00 × 10-3). LDSC detected a genetic correlation between short TL and insomnia (Rg = -9.27 × 10-2, P = 8.00 × 10-4). We found no evidence supporting significant causal association between insomnia and TL in IVW method (b = -5.95 × 10-3, P = 0.57), with horizontal pleiotropy and heterogeneity tests indicating the validity of our MR study. Finally, rs12638862 was classified as colocalized by COLOC (PP4 = 0.99), and TERC may be involved in regulating the association between insomnia and TL. CONCLUSIONS Our study found no evidence for causal association between insomnia and TL in individuals of European ancestry. We detected a candidate gene associated with both insomnia and TL, providing novel clues for understanding the roles of this association.
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Affiliation(s)
- Dan He
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
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18
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Borie R, Cardwell J, Konigsberg IR, Moore CM, Zhang W, Sasse SK, Gally F, Dobrinskikh E, Walts A, Powers J, Brancato J, Rojas M, Wolters PJ, Brown KK, Blackwell TS, Nakanishi T, Richards JB, Gerber AN, Fingerlin TE, Sachs N, Pulit SL, Zappala Z, Schwartz DA, Yang IV. Colocalization of Gene Expression and DNA Methylation with Genetic Risk Variants Supports Functional Roles of MUC5B and DSP in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2022; 206:1259-1270. [PMID: 35816432 PMCID: PMC9746850 DOI: 10.1164/rccm.202110-2308oc] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Rationale: Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). Objectives: To determine functional relevance of the 10 IPF-associated common genetic variants we previously identified. Methods: We performed expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) mapping, followed by co-localization of eQTL and mQTL with genetic association signals and functional validation by luciferase reporter assays. Illumina multi-ethnic genotyping arrays, mRNA sequencing, and Illumina 850k methylation arrays were performed on lung tissue of participants with IPF (234 RNA and 345 DNA samples) and non-diseased controls (188 RNA and 202 DNA samples). Measurements and Main Results: Focusing on genetic variants within 10 IPF-associated genetic loci, we identified 27 eQTLs in controls and 24 eQTLs in cases (false-discovery-rate-adjusted P < 0.05). Among these signals, we identified associations of lead variants rs35705950 with expression of MUC5B and rs2076295 with expression of DSP in both cases and controls. mQTL analysis identified CpGs in gene bodies of MUC5B (cg17589883) and DSP (cg08964675) associated with the lead variants in these two loci. We also demonstrated strong co-localization of eQTL/mQTL and genetic signal in MUC5B (rs35705950) and DSP (rs2076295). Functional validation of the mQTL in MUC5B using luciferase reporter assays demonstrates that the CpG resides within a putative internal repressor element. Conclusions: We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.
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Affiliation(s)
| | | | | | - Camille M. Moore
- Department of Biostatistics and Bioinformatics and
- Center for Genes, Environment, and Health
| | | | | | - Fabienne Gally
- Department of Medicine
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | | | | | | | | | - Mauricio Rojas
- Department of Internal Medicine, Ohio State College of Medicine, The Ohio State University, Columbus, Ohio
| | - Paul J. Wolters
- Department of Medicine, University of California, San Francisco, California
| | | | - Timothy S. Blackwell
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tomoko Nakanishi
- Department of Human Genetics, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Canada
| | - J. Brent Richards
- Department of Human Genetics, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Canada
| | - Anthony N. Gerber
- Department of Medicine
- Department of Medicine, and
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Tasha E. Fingerlin
- Department of Biostatistics and Bioinformatics and
- Center for Genes, Environment, and Health
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Norman Sachs
- Cell Biology, Vertex Pharmaceuticals, San Diego, California; and
| | - Sara L. Pulit
- Computational Genomics, Vertex Pharmaceuticals, Boston, Massachusetts
| | - Zachary Zappala
- Computational Genomics, Vertex Pharmaceuticals, Boston, Massachusetts
| | - David A. Schwartz
- Department of Medicine
- Department of Microbiology and Immunology, University of Colorado Anschutz Medical Campus; Aurora, Colorado
| | - Ivana V. Yang
- Department of Medicine
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado
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19
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Wood AC, Arora A, Newell M, Bland VL, Zhou J, Pirastu N, Ordovas JM, Klimentidis YC. Identification of genetic loci simultaneously associated with multiple cardiometabolic traits. Nutr Metab Cardiovasc Dis 2022; 32:1027-1034. [PMID: 35168826 PMCID: PMC9275655 DOI: 10.1016/j.numecd.2022.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 12/09/2021] [Accepted: 01/04/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS Cardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits. METHODS AND RESULTS We conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high- density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N = 356,574-456,823). Multiple loci reached genome-wide levels of significance (N = 145-333) for each trait, but only four loci (in/near VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P < 5 × 10-8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses. GRB14-COBLL1 showed the most consistent replication, revealing nominally significant associations (P < 0.05) with all traits except DBP. CONCLUSIONS Our analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.
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Affiliation(s)
- Alexis C Wood
- USDA/ARS Children's Nutrition Research Center, 1100 Bates Avenue, Houston, TX, USA.
| | - Amit Arora
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Michelle Newell
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Victoria L Bland
- Division of Geriatric Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jin Zhou
- Department of Biostatistics, University of California, Los Angeles, CA, USA
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; IMDEA-Food, Madrid, Spain
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA; BIO5 Institute, University of Arizona, Tucson, AZ, USA
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20
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Ghasemi S, Becker T, Grabe HJ, Teumer A. Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method. Front Genet 2022; 13:997302. [PMID: 36386835 PMCID: PMC9660290 DOI: 10.3389/fgene.2022.997302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10-8. Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci.
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Affiliation(s)
- Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Tim Becker
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.,German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald, Greifswald, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
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21
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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22
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Zhu M, Yin P, Hu F, Jiang J, Yin L, Li Y, Wang S. Integrating genome-wide association and transcriptome prediction model identifies novel target genes for osteoporosis. Osteoporos Int 2021; 32:2493-2503. [PMID: 34142171 PMCID: PMC8608767 DOI: 10.1007/s00198-021-06024-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 05/31/2021] [Indexed: 12/12/2022]
Abstract
UNLABELLED In this study, we integrated large-scale GWAS summary data and used the predicted transcriptome-wide association study method to discover novel genes associated with osteoporosis. We identified 204 candidate genes, which provide novel clues for understanding the genetic mechanism of osteoporosis and indicate potential therapeutic targets. INTRODUCTION Osteoporosis is a highly polygenetic disease characterized by low bone mass and deterioration of the bone microarchitecture. Our objective was to discover novel candidate genes associated with osteoporosis. METHODS To identify potential causal genes of the associated loci, we investigated trait-gene expression associations using the transcriptome-wide association study (TWAS) method. This method directly imputes gene expression effects from genome-wide association study (GWAS) data using a statistical prediction model trained on GTEx reference transcriptome data. We then performed a colocalization analysis to evaluate the posterior probability of biological patterns: associations characterized by a single causal variant or multiple distinct causal variants. Finally, a functional enrichment analysis of gene sets was performed using the VarElect and CluePedia tools, which assess the causal relationships between genes and a disease and search for potential gene's functional pathways. The osteoporosis-associated genes were further confirmed based on the differentially expressed genes profiled from mRNA expression data of bone tissue. RESULTS Our analysis identified 204 candidate genes, including 154 genes that have been previously associated with osteoporosis, 50 genes that have not been previously discovered. A biological function analysis found that 20 of the candidate genes were directly associated with osteoporosis. Further analysis of multiple gene expression profiles showed that 15 genes were differentially expressed in patients with osteoporosis. Among these, SLC11A2, MAP2K5, NFATC4, and HSP90B1 were enriched in four pathways, namely, mineral absorption pathway, MAPK signaling pathway, Wnt signaling pathway, and PI3K-Akt signaling pathway, which indicates a causal relationship with the occurrence of osteoporosis. CONCLUSIONS We demonstrated that transcriptome fine-mapping identifies more osteoporosis-related genes and provides key insight into the development of novel targeted therapeutics for the treatment of osteoporosis.
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Affiliation(s)
- M Zhu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - P Yin
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - F Hu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - J Jiang
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - L Yin
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Y Li
- AnLan AI, Shenzhen, China
| | - S Wang
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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23
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Claussnitzer M, Susztak K. Gaining insight into metabolic diseases from human genetic discoveries. Trends Genet 2021; 37:1081-1094. [PMID: 34315631 PMCID: PMC8578350 DOI: 10.1016/j.tig.2021.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 12/30/2022]
Abstract
Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.
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Affiliation(s)
- Melina Claussnitzer
- Beth Israel Deaconess Medical Center, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Katalin Susztak
- Department of Medicine and Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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24
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Kwong A, Boughton AP, Wang M, VandeHaar P, Boehnke M, Abecasis G, Kang HM. FIVEx: an interactive eQTL browser across public datasets. Bioinformatics 2021; 38:559-561. [PMID: 34459872 PMCID: PMC8723151 DOI: 10.1093/bioinformatics/btab614] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 06/29/2021] [Accepted: 08/26/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Expression quantitative trait loci (eQTLs) characterize the associations between genetic variation and gene expression to provide insights into tissue-specific gene regulation. Interactive visualization of tissue-specific eQTLs or splice QTLs (sQTLs) can facilitate our understanding of functional variants relevant to disease-related traits. However, combining the multi-dimensional nature of eQTLs/sQTLs into a concise and informative visualization is challenging. Existing QTL visualization tools provide useful ways to summarize the unprecedented scale of transcriptomic data but are not necessarily tailored to answer questions about the functional interpretations of trait-associated variants or other variants of interest. We developed FIVEx, an interactive eQTL/sQTL browser with an intuitive interface tailored to the functional interpretation of associated variants. It features the ability to navigate seamlessly between different data views while providing relevant tissue- and locus-specific information to offer users a better understanding of population-scale multi-tissue transcriptomic profiles. Our implementation of the FIVEx browser on the EBI eQTL catalogue, encompassing 16 publicly available RNA-seq studies, provides important insights for understanding potential tissue-specific regulatory mechanisms underlying trait-associated signals. AVAILABILITY AND IMPLEMENTATION A FIVEx instance visualizing EBI eQTL catalogue data can be found at https://fivex.sph.umich.edu. Its source code is open source under an MIT license at https://github.com/statgen/fivex. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Mukai Wang
- Department of Biostatistics, The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter VandeHaar
- Department of Biostatistics, The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Department of Biostatistics, The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gonçalo Abecasis
- Department of Biostatistics, The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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25
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Li J, Tang F, Si S, Wang B, Xue F. Integration analysis of GWAS and expression quantitative trait loci to identify candidate genes and pathways for clozapine-related neutropaenia. Br J Clin Pharmacol 2021; 88:1904-1912. [PMID: 34409637 DOI: 10.1111/bcp.15043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 12/23/2022] Open
Abstract
AIMS Little is known about the genetic basis of clozapine-related neutropaenia. This study aims to explore candidate genes and pathways involved in clozapine-related neutropaenia. METHODS This study conducted a two-stage integrative analysis of the summary statistics from the genome-wide association study (GWAS, n = 552) of the lowest absolute neutrophil count (ANC) during clozapine treatment and the summary data of the expressed quantitative trait locus (eQTL). First, we use the probabilistic Mendelian randomization (PMR-Egger) to identify genes whose expression is causally related to ANC, and then use Bayesian co-localization analysis to investigate whether there are shared causal variants between them [posterior probability for hypotheses 4 (PP.H4) > 0.80]. Finally, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore the pathways that may be associated with ANC during clozapine treatment. RESULTS PMR-Egger analysis identified 146 genes that may be causally associated with ANC after Bonferroni correction (P-value < 3.25e-6). Bayesian co-localization analysis identified six further genes whose gene expression shared common variants with ANC, including NT5E (PP.H4 = 0.96), GLDC (PP.H4 = 0.82), NUDT17 (PP.H4 = 0.88), MSH4 (PP.H4 = 0.88), PTER (PP.H4 = 0.89) and SERPINB6 (PP.H4 = 0.83). Enrichment analysis identified 52 GO terms and seven pathways associated with ANC, such as NAD metabolic process, drug catabolic process and glyoxylate and dicarboxylate metabolism. CONCLUSION This study identified multiple candidate genes and pathways that may be involved in clozapine-related neutropaenia, providing novel clues for the mechanism of clozapine-related neutropaenia.
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Affiliation(s)
- Jiqing Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.,Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Fang Tang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Shucheng Si
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.,Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Bojie Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.,Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.,Healthcare Big Data Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
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Bagchi DP, MacDougald OA. Wnt Signaling: From Mesenchymal Cell Fate to Lipogenesis and Other Mature Adipocyte Functions. Diabetes 2021; 70:1419-1430. [PMID: 34155042 PMCID: PMC8336005 DOI: 10.2337/dbi20-0015] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/20/2021] [Indexed: 12/14/2022]
Abstract
Wnt signaling is an ancient and evolutionarily conserved pathway with fundamental roles in the development of adipose tissues. Roles of this pathway in mesenchymal stem cell fate determination and differentiation have been extensively studied. Indeed, canonical Wnt signaling is a significant endogenous inhibitor of adipogenesis and promoter of other cell fates, including osteogenesis, chondrogenesis, and myogenesis. However, emerging genetic evidence in both humans and mice suggests central roles for Wnt signaling in body fat distribution, obesity, and metabolic dysfunction. Herein, we highlight recent studies that have begun to unravel the contributions of various Wnt pathway members to critical adipocyte functions, including carbohydrate and lipid metabolism. We further explore compelling evidence of complex and coordinated interactions between adipocytes and other cell types within adipose tissues, including stromal, immune, and endothelial cells. Given the evolutionary conservation and ubiquitous cellular distribution of this pathway, uncovering the contributions of Wnt signaling to cell metabolism has exciting implications for therapeutic intervention in widespread pathologic states, including obesity, diabetes, and cancers.
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Affiliation(s)
- Devika P Bagchi
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI
| | - Ormond A MacDougald
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI
- Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
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27
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Bouchard C. Genetics of Obesity: What We Have Learned Over Decades of Research. Obesity (Silver Spring) 2021; 29:802-820. [PMID: 33899337 DOI: 10.1002/oby.23116] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/14/2022]
Abstract
There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.
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Affiliation(s)
- Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
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28
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Patel D, Zhang X, Farrell JJ, Chung J, Stein TD, Lunetta KL, Farrer LA. Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue. Transl Psychiatry 2021; 11:250. [PMID: 33907181 PMCID: PMC8079392 DOI: 10.1038/s41398-021-01373-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 03/24/2021] [Accepted: 04/08/2021] [Indexed: 02/02/2023] Open
Abstract
Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of "proxy" genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.
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Affiliation(s)
- Devanshi Patel
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - John J Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Jaeyoon Chung
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology & Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Lindsay A Farrer
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA.
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA.
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Departments of Neurology and Ophthalmology, Boston University School of Medicine, Boston, MA, USA.
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
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29
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Yang J, Yu X, Zhu G, Wang R, Lou S, Zhu W, Fu C, Liu J, Fan L, Li D, Shao Q, Ma L, Wang L, Wang Z, Pan Y. Integrating GWAS and eQTL to predict genes and pathways for non-syndromic cleft lip with or without palate. Oral Dis 2020; 27:1747-1754. [PMID: 33128317 DOI: 10.1111/odi.13699] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/14/2020] [Accepted: 10/16/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To explore susceptibility genes and pathways for non-syndromic cleft lip with or without cleft palate (NSCL/P). MATERIALS AND METHODS Two genome-wide association studies (GWAS) datasets, including 858 NSCL/P cases and 1,248 controls, were integrated with expression quantitative trait loci (eQTL) dataset identified by Genotype-Tissue Expression (GTEx) project in whole-blood samples. The expression of the candidate genes in mouse orofacial development was inquired from FaceBase. Protein-protein interaction (PPI) network was visualized to identify protein functions. Go and KEGG pathway analyses were performed to explore the underlying risk pathways. RESULTS A total of 233 eQTL single-nucleotide polymorphisms (SNPs) in 432 candidate genes were identified to be associated with the risk of NSCL/P. One hundred and eighty-three susceptible genes were expressed in mouse orofacial development according to FaceBase. PPI network analysis highlighted that these genes involved in ubiquitin-mediated proteolysis (KCTD7, ASB1, UBOX5, ANAPC4) and DNA synthesis (XRCC3, RFC3, KAT5, RHNO1) were associated with the risk of NSCL/P. GO and KEGG pathway analyses revealed that the fatty acid metabolism pathway (ACADL, HSD17B12, ACSL5, PPT1, MCAT) played an important role in the development of NSCL/P. CONCLUSIONS Our results identified novel susceptibility genes and pathways associated with the development of NSCL/P.
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Affiliation(s)
- Jing Yang
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Xin Yu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Guirong Zhu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Ruimin Wang
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
| | - Shu Lou
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Weihao Zhu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Chengyi Fu
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Jinsuo Liu
- Yifangming (Beijing) Technology Co., Ltd, Beijing, China
| | - Liwen Fan
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Dandan Li
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Qinghua Shao
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Lan Ma
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China
| | - Lin Wang
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Zhendong Wang
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Yongchu Pan
- Jiangsu Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, China.,Department of Orthodontics, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
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30
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Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, Robinson J, Richardson TG, Staley JR, Elsworth B, Burgess S, Sun BB, Danesh J, Runz H, Maranville JC, Martin HM, Yarmolinsky J, Laurin C, Holmes MV, Liu JZ, Estrada K, Santos R, McCarthy L, Waterworth D, Nelson MR, Smith GD, Butterworth AS, Hemani G, Scott RA, Gaunt TR. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet 2020; 52:1122-1131. [PMID: 32895551 PMCID: PMC7610464 DOI: 10.1038/s41588-020-0682-6] [Citation(s) in RCA: 270] [Impact Index Per Article: 67.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 07/24/2020] [Indexed: 01/23/2023]
Abstract
The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
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Affiliation(s)
- Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
| | - Valeriia Haberland
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Denis Baird
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Venexia Walker
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Mark R Hurle
- Human Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Pau Erola
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Yi Liu
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Shan Luo
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, Hong Kong, China
| | - Jamie Robinson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - James R Staley
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin Elsworth
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Benjamin B Sun
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Heiko Runz
- Translational Biology, Biogen, Cambridge, MA, USA
| | - Joseph C Maranville
- Informatics and Predictive Sciences, Celgene Corporation, Cambridge, MA, USA
| | - Hannah M Martin
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - James Yarmolinsky
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Charles Laurin
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Jimmy Z Liu
- Translational Biology, Biogen, Cambridge, MA, USA
| | | | - Rita Santos
- Functional Genomics, GlaxoSmithKline, Stevenage, Hertfordshire, UK
| | | | | | | | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Adam S Butterworth
- Proteome MR writing group
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- BHF Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK
- Proteome MR writing group
| | - Robert A Scott
- Proteome MR writing group, .
- Human Genetics, GlaxoSmithKline, Stevenage, UK.
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.
- Proteome MR writing group, .
- NIHR Bristol Biomedical Research Centre, Bristol, UK.
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31
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Finkbeiner S. Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease. Neurobiol Dis 2020; 146:105088. [PMID: 32977020 PMCID: PMC7686089 DOI: 10.1016/j.nbd.2020.105088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 12/03/2022] Open
Abstract
Human genetics provides unbiased insights into the causes of human disease, which can be used to create a foundation for effective ways to more accurately diagnose patients, stratify patients for more successful clinical trials, discover and develop new therapies, and ultimately help patients choose the safest and most promising therapeutic option based on their risk profile. But the process for translating basic observations from human genetics studies into pathogenic disease mechanisms and treatments is laborious and complex, and this challenge has particularly slowed the development of interventions for neurodegenerative disease. In this review, we discuss the many steps in the process, the important considerations at each stage, and some of the latest tools and technologies that are available to help investigators translate insights from human genetics into diagnostic and therapeutic strategies that will lead to the sort of advances in clinical care that make a difference for patients.
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Affiliation(s)
- Steven Finkbeiner
- Center for Systems and Therapeutics, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA; Departments of Neurology and Physiology, University of Califorina, San Francisco, CA 94158, USA.
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32
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He B, Shi J, Wang X, Jiang H, Zhu HJ. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol 2020; 18:97. [PMID: 32778093 PMCID: PMC7418398 DOI: 10.1186/s12915-020-00830-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 07/16/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers. RESULTS We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in post-transcriptional regulation of protein expression in human livers. CONCLUSIONS We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.
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Affiliation(s)
- Bing He
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Jian Shi
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Xinwen Wang
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA
| | - Hui Jiang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hao-Jie Zhu
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, 428 Church Street, Room 4565 NUB, Ann Arbor, MI, 48109-1065, USA.
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